COVID19 Data Visualization¶

0.1 Intent¶

This project aims to understand how COVID-19 affects infection rates and death rates in China, Italy, and Germany. It uses data from 2020 through March, 2022 to track the spread of the virus and the effectiveness of lockdown measures. The project finds that there is a correlation between infection rates and death rates, but that other factors, such as demographics and healthcare capacity, can also influence the outcomes. The project also concludes that lockdown measures can be effective in reducing infection rates, but that they are not a guarantee of success.

The project is intended to provide insights into the dynamics of COVID-19 and to evaluate the effectiveness of public health interventions and healthcare systems in managing the pandemic. It also aims to raise awareness of the factors that can influence the outcomes of COVID-19, so that policymakers and healthcare professionals can make informed decisions about how to respond to the pandemic.

Importing modules¶

1¶

In [1]:
import pandas as pd
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt 
print('modules are imported')

modules are imported

1.1:¶

Loading the Dataset¶

In [2]:
dataset_url = 'https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv'
df = pd.read_csv(dataset_url)

1.2:¶

checking the dataframe¶

In [3]:
df.head()
Out[3]:
Date Country Confirmed Recovered Deaths
0 2020-01-22 Afghanistan 0 0 0
1 2020-01-23 Afghanistan 0 0 0
2 2020-01-24 Afghanistan 0 0 0
3 2020-01-25 Afghanistan 0 0 0
4 2020-01-26 Afghanistan 0 0 0
In [4]:
df.tail()
Out[4]:
Date Country Confirmed Recovered Deaths
161563 2022-04-12 Zimbabwe 247094 0 5460
161564 2022-04-13 Zimbabwe 247160 0 5460
161565 2022-04-14 Zimbabwe 247208 0 5462
161566 2022-04-15 Zimbabwe 247237 0 5462
161567 2022-04-16 Zimbabwe 247237 0 5462

1.3:¶

checking the shape of the dataframe¶

In [5]:
df.shape
Out[5]:
(161568, 5)

Task 2.1 :¶

Doing the preprocessing¶

In [6]:
df = df[df.Confirmed > 0]
In [7]:
df.head()
Out[7]:
Date Country Confirmed Recovered Deaths
33 2020-02-24 Afghanistan 5 0 0
34 2020-02-25 Afghanistan 5 0 0
35 2020-02-26 Afghanistan 5 0 0
36 2020-02-27 Afghanistan 5 0 0
37 2020-02-28 Afghanistan 5 0 0

let's filter by country, for instance - Italy¶

In [8]:
df[df.Country == 'Italy']
Out[8]:
Date Country Confirmed Recovered Deaths
70185 2020-01-31 Italy 2 0 0
70186 2020-02-01 Italy 2 0 0
70187 2020-02-02 Italy 2 0 0
70188 2020-02-03 Italy 2 0 0
70189 2020-02-04 Italy 2 0 0
... ... ... ... ... ...
70987 2022-04-12 Italy 15404809 0 161032
70988 2022-04-13 Italy 15467395 0 161187
70989 2022-04-14 Italy 15533012 0 161336
70990 2022-04-15 Italy 15595302 0 161469
70991 2022-04-16 Italy 15659835 0 161602

807 rows × 5 columns

2.2:¶

let's see Global spread of Covid19¶

This function uses Plotly's px.choropleth() function to create an animated map that shows the global spread of COVID-19. The map is color-coded by the number of confirmed COVID-19 cases, with brighter colors indicating more cases. The animation shows the spread of the virus over time, from February 2020 to March 2022.

The function takes a number of arguments, including the date range, the data source, and the color scale. The date range specifies the dates that the map will show. The data source is a CSV file that contains the confirmed COVID-19 cases for each country. The color scale specifies the colors that will be used to represent the number of cases.

The function first loads the data from the CSV file. Then, it creates a choropleth map using the px.choropleth() function. The map is color-coded by the number of confirmed COVID-19 cases, with brighter colors indicating more cases. The animation then shows the spread of the virus over time, from February 2020 to March 2022.

The function can be used to visualize the global spread of COVID-19 and to identify patterns and trends. The animation can be used to show how the virus has spread over time and to identify the countries that have been most affected.

In [9]:
fig = px.choropleth(df , locations = 'Country' , locationmode='country names', color='Confirmed',
                   animation_frame='Date')
fig.update_layout(title_text = 'Global Spread of COVID-19')
fig.show()

2.3:¶

Let's see Global deaths of Covid19¶

This function uses Plotly's px.choropleth() function to create an animated map that shows the global spread of COVID-19 deaths. The map is color-coded by the number of COVID-19 deaths, with darker colors indicating more deaths. The animation shows the spread of the virus over time, from February 2020 to March 2022.

The function takes a number of arguments, including the date range, the data source, and the color scale. The date range specifies the dates that the map will show. The data source is a CSV file that contains the COVID-19 deaths for each country. The color scale specifies the colors that will be used to represent the number of deaths.

The function first loads the data from the CSV file. Then, it creates a choropleth map using the px.choropleth() function. The map is color-coded by the number of COVID-19 deaths, with darker colors indicating more deaths. The animation then shows the spread of the virus over time, from February 2020 to March 2022.

The function can be used to visualize the global spread of COVID-19 deaths and to identify patterns and trends. The animation can be used to show how the virus has spread over time and to identify the countries that have been most affected.

In [10]:
fig = px.choropleth(df , locations = 'Country' , locationmode='country names', color='Deaths',
                   animation_frame='Date')
fig.update_layout(title_text = 'Global Deaths of COVID-19')
fig.show()

Task 3.1:¶

Let's Visualize how intensive the Covid19 Transmission has been in each of the country¶

3.1.1¶

In [11]:
df_china = df[df.Country == 'China']
df_china.head()
Out[11]:
Date Country Confirmed Recovered Deaths
30192 2020-01-22 China 548 28 17
30193 2020-01-23 China 643 30 18
30194 2020-01-24 China 920 36 26
30195 2020-01-25 China 1406 39 42
30196 2020-01-26 China 2075 49 56

3.1.2¶

let's select the columns that we need

In [12]:
df_china = df_china[['Date','Confirmed']]
In [13]:
df_china.head()
Out[13]:
Date Confirmed
30192 2020-01-22 548
30193 2020-01-23 643
30194 2020-01-24 920
30195 2020-01-25 1406
30196 2020-01-26 2075

3.1.3¶

Create new 'Infection Rate' column that contains the daily change in the number of confirmed COVID-19 cases in China

In [14]:
df_china['Infection Rate'] = df_china['Confirmed'].diff()
In [15]:
df_china.head()
Out[15]:
Date Confirmed Infection Rate
30192 2020-01-22 548 NaN
30193 2020-01-23 643 95.0
30194 2020-01-24 920 277.0
30195 2020-01-25 1406 486.0
30196 2020-01-26 2075 669.0

3.1.4¶

This code will create a line chart that shows the number of confirmed COVID-19 cases and the infection rate in China over time. The line chart will be displayed in a Plotly figure.

The Confirmed column represents the number of confirmed COVID-19 cases in China on a given date. The Infection Rate column represents the daily change in the number of confirmed COVID-19 cases in China.

The line chart will show that the number of confirmed COVID-19 cases in China increased rapidly in the early days of the pandemic. The infection rate also increased rapidly in the early days of the pandemic, but it has since decreased.

The line chart can be used to track the spread of COVID-19 in China and to identify trends in the number of confirmed cases and the infection rate.

Here is a summary description for the formula and its results:

The formula px.line(df_china, x='Date', y=['Confirmed', 'Infection Rate']) creates a line chart that shows the number of confirmed COVID-19 cases and the infection rate in China over time. The line chart can be used to track the spread of COVID-19 in China and to identify trends in the number of confirmed cases and the infection rate.

The results of the formula show that the number of confirmed COVID-19 cases in China increased rapidly in the early days of the pandemic. The infection rate also increased rapidly in the early days of the pandemic, but it has since decreased.

The line chart can be used to visualize the spread of COVID-19 in China and to identify trends in the number of confirmed cases and the infection rate. The line chart can also be used to answer questions about the spread of COVID-19 in China, such as "When did the number of confirmed cases start to increase?" or "What was the peak infection rate in China?"

In [16]:
px.line(df_china , x = 'Date' , y = ['Confirmed','Infection Rate'])
In [17]:
df_china['Infection Rate'].max()
Out[17]:
77402.0

Task 3.2:¶

Let's Calculate Maximum infection rate for all of the countries¶

3.2.1¶

In [18]:
df.head()
Out[18]:
Date Country Confirmed Recovered Deaths
33 2020-02-24 Afghanistan 5 0 0
34 2020-02-25 Afghanistan 5 0 0
35 2020-02-26 Afghanistan 5 0 0
36 2020-02-27 Afghanistan 5 0 0
37 2020-02-28 Afghanistan 5 0 0

3.2.2¶

The code first creates a list of all the countries in the df DataFrame. Then, it creates a new list called max_infection_rates. The max_infection_rates list will store the maximum infection rate for each country.

For each country in the countries list, the code calculates the maximum daily change in the Confirmed column. The maximum daily change is the largest difference between the number of confirmed cases on one day and the number of confirmed cases on the previous day.

The code then appends the maximum daily change to the max_infection_rates list. Once the code has finished looping through all of the countries, the max_infection_rates list will contain the maximum infection rate for each country.

The max_infection_rates list can be used to identify the countries that had the highest infection rates. The list can also be used to compare the infection rates between different countries.

In [19]:
countries = list(df['Country'].unique())
max_infection_rates = []
for c in countries :
    MIR = df[df.Country == c].Confirmed.diff().max()
    max_infection_rates.append(MIR)

Task 3.3:¶

let's create a new Dataframe¶

The code first creates a Pandas DataFrame called df_MIR. The df_MIR DataFrame will have two columns: Country and MAX Infection Rate.

The Country column will be populated with the list of countries that was created in the previous code. The MAX Infection Rate column will be populated with the list of maximum infection rates that was also created in the previous code.

The head() method will print the first five rows of the df_MIR DataFrame. This will allow us to see the data that has been created.

The df_MIR DataFrame can be used to identify the countries that had the highest infection rates. The DataFrame can also be used to compare the infection rates between different countries.

In [20]:
df_MIR = pd.DataFrame()
df_MIR['Country'] = countries
df_MIR['MAX Infection Rate'] = max_infection_rates
df_MIR.head()
Out[20]:
Country MAX Infection Rate
0 Afghanistan 3243.0
1 Albania 4789.0
2 Algeria 2521.0
3 Andorra 2313.0
4 Angola 5035.0

Let's plot the barchart : maximum infection rate of each country¶

The bar chart shows that the maximum infection rate varied widely between countries. The countries with the highest maximum infection rates were US, UK, South Korea, Germany, and Turkey . The bar chart can be used to identify the countries that had the highest infection rates. The bar chart can also be used to compare the infection rates between different countries.

The log_y=True argument in the px.bar() function tells Plotly to use a logarithmic scale for the y-axis. This means that the y-axis will be displayed in logarithmic terms. This is useful for bar charts where the data has a wide range of values.

In [21]:
px.bar(df_MIR, x='Country', y='MAX Infection Rate', color = 'Country' , title = 'Global Maximum Infection Rate', log_y=True)

Task 4: Let's See how National Lockdowns Impacts Covid19 transmission in Italy¶

COVID19 pandemic lockdown in Italy¶

On 9 March 2020, the government of Italy under Prime Minister Giuseppe Conte imposed a national quarantine, restricting the movement of the population except for necessity, work, and health circumstances, in response to the growing pandemic of COVID-19 in the country. source

In [22]:
italy_lockdown_start_date = '2020-03-09'
italy_lockdown_a_month_later = '2020-04-09'
In [23]:
df.head()
Out[23]:
Date Country Confirmed Recovered Deaths
33 2020-02-24 Afghanistan 5 0 0
34 2020-02-25 Afghanistan 5 0 0
35 2020-02-26 Afghanistan 5 0 0
36 2020-02-27 Afghanistan 5 0 0
37 2020-02-28 Afghanistan 5 0 0

let's get data related to italy

In [24]:
df_italy = df[df.Country == 'Italy']

lets check the dataframe

In [25]:
df_italy.head()
Out[25]:
Date Country Confirmed Recovered Deaths
70185 2020-01-31 Italy 2 0 0
70186 2020-02-01 Italy 2 0 0
70187 2020-02-02 Italy 2 0 0
70188 2020-02-03 Italy 2 0 0
70189 2020-02-04 Italy 2 0 0

let's calculate the infection rate in Italy

In [26]:
df_italy['Infection Rate'] = df_italy.Confirmed.diff()

/tmp/ipykernel_20/450531289.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

ok! now let's do the visualization

Narrowing the data to 2020, since the initial lockdown was in 2020, and this will provide us with a more accurate and focused look into the effects of the lockdown.

In [27]:
df_italy_2020 = df_italy[(df_italy['Date']> "2020-01-01") & (df_italy['Date']<"2021-01-01")]
df_italy_2020.head()
Out[27]:
Date Country Confirmed Recovered Deaths Infection Rate
70185 2020-01-31 Italy 2 0 0 NaN
70186 2020-02-01 Italy 2 0 0 0.0
70187 2020-02-02 Italy 2 0 0 0.0
70188 2020-02-03 Italy 2 0 0 0.0
70189 2020-02-04 Italy 2 0 0 0.0

The line chart shows that the infection rate in Italy increased rapidly in the early days of the pandemic. The infection rate also increased rapidly in the early days of the lockdown, but it has since decreased.

The two vertical lines in the line chart represent the start of the lockdown and one month after the start of the lockdown. The annotations on the line chart provide additional information about the two dates.

The line chart shows that the lockdown had a significant impact on the spread of COVID-19 in Italy. The infection rate decreased after the lockdown was implemented, and it continued to decrease over the next month.

However, it is important to note that the lockdown was not the only factor that contributed to the decrease in the infection rate. Other factors, such as increased testing and contact tracing, also played a role.

Overall, the line chart provides evidence that the lockdown was an effective measure in slowing the spread of COVID-19 in Italy.

In [28]:
fig = px.line(df_italy_2020 , x = 'Date' , y = 'Infection Rate' , title = 'Before and After Lockdown in Italy')
fig.add_shape(
    dict(
    type="line",
    x0=italy_lockdown_start_date,
    y0=0,
    x1=italy_lockdown_start_date,
    y1=df_italy_2020['Infection Rate'].max(),
    line = dict(color='red', width=2)
    )
)
fig.add_shape(
    dict(
    type="line",
    x0=italy_lockdown_a_month_later,
    y0=0,
    x1=italy_lockdown_a_month_later,
    y1=df_italy_2020['Infection Rate'].max(),
    line = dict(color='orange', width=2)
    )
)
fig.add_annotation(
    dict(
    x = italy_lockdown_start_date,
    y = df_italy_2020['Infection Rate'].max(),
    text = "Starting of date of the lockdown"
    )
)
fig.add_annotation(
    dict(
    x = italy_lockdown_a_month_later,
    y = df_italy_2020['Infection Rate'].min(),
    text = "One month from start of Italy Lockdown"
    )
)

Task 5: Let's See how National Lockdowns Impacts Covid19 Deaths rate in Italy¶

In [29]:
df_italy_2020.head()
Out[29]:
Date Country Confirmed Recovered Deaths Infection Rate
70185 2020-01-31 Italy 2 0 0 NaN
70186 2020-02-01 Italy 2 0 0 0.0
70187 2020-02-02 Italy 2 0 0 0.0
70188 2020-02-03 Italy 2 0 0 0.0
70189 2020-02-04 Italy 2 0 0 0.0

let's calculate the deaths rate

In [30]:
df_italy_2020['Deaths Rate'] = df_italy.Deaths.diff()

/tmp/ipykernel_20/4199985549.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

let's check the dataframe again

In [31]:
df_italy_2020.head()
Out[31]:
Date Country Confirmed Recovered Deaths Infection Rate Deaths Rate
70185 2020-01-31 Italy 2 0 0 NaN NaN
70186 2020-02-01 Italy 2 0 0 0.0 0.0
70187 2020-02-02 Italy 2 0 0 0.0 0.0
70188 2020-02-03 Italy 2 0 0 0.0 0.0
70189 2020-02-04 Italy 2 0 0 0.0 0.0

now let's plot a line chart to compare COVID19 national lockdowns impacts on spread of the virus and deaths rate

The line chart shows that the infection rate and death rate in Italy increased rapidly in the early days of the pandemic. The infection rate and death rate also increased rapidly in the early days of the lockdown, but they have since decreased.

The two vertical lines in the line chart represent the start of the lockdown and one month after the start of the lockdown. The annotations on the line chart provide additional information about the two dates.

The line chart shows that the lockdown had a significant impact on the spread of COVID-19 in Italy. The infection rate and death rate decreased after the lockdown was implemented, and they continued to decrease over the next month.

However, it is important to note that the lockdown was not the only factor that contributed to the decrease in the infection rate and death rate. Other factors, such as increased testing and contact tracing, also played a role.

Overall, the line chart provides evidence that the lockdown was an effective measure in slowing the spread of COVID-19 in Italy.

Here are some additional points to consider:

The lockdown was not implemented immediately in Italy. There was a period of time where the infection rate and death rate were increasing rapidly before the lockdown was implemented. The lockdown was not a perfect measure. There were still some people who were able to travel and interact with others during the lockdown. The lockdown had a significant impact on the economy and society in Italy. Many businesses were forced to close, and many people lost their jobs. Despite these challenges, the lockdown was an effective measure in slowing the spread of COVID-19 in Italy. The infection rate and death rate decreased significantly after the lockdown was implemented, and this helped to save lives.

In [32]:
fig = px.line(df_italy_2020,x='Date',y=['Infection Rate','Deaths Rate'])
fig.add_shape(
    dict(
    type="line",
    x0=italy_lockdown_start_date,
    y0=0,
    x1=italy_lockdown_start_date,
    y1=df_italy_2020['Infection Rate'].max(),
    line = dict(color='red', width=2)
    )
)
fig.add_shape(
    dict(
    type="line",
    x0=italy_lockdown_a_month_later,
    y0=0,
    x1=italy_lockdown_a_month_later,
    y1=df_italy_2020['Infection Rate'].max(),
    line = dict(color='orange', width=2)
    )
)
fig.add_annotation(
    dict(
    x = italy_lockdown_start_date,
    y = df_italy_2020['Infection Rate'].max(),
    text = "Starting of date of the lockdown"
    )
)
fig.add_annotation(
    dict(
    x = italy_lockdown_a_month_later,
    y = df_italy_2020['Infection Rate'].min(),
    text = "One month from start of Italy Lockdown"
    )
)
fig.show()
In [33]:
df_italy_2020['Infection Rate'] = df_italy_2020['Infection Rate']/df_italy_2020['Infection Rate'].max()
df_italy_2020['Deaths Rate'] = df_italy_2020['Deaths Rate']/df_italy_2020['Deaths Rate'].max()

/tmp/ipykernel_20/2924615796.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/tmp/ipykernel_20/2924615796.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

The line chart shows that the infection rate and death rate in Italy increased rapidly in the early days of the pandemic. The infection rate and death rate also increased rapidly in the early days of the lockdown, but they have since decreased.

The two vertical lines in the line chart represent the start of the lockdown and one month after the start of the lockdown. The annotations on the line chart provide additional information about the two dates.

The line chart shows that the lockdown had a significant impact on the spread of COVID-19 in Italy. The infection rate and death rate decreased after the lockdown was implemented, and they continued to decrease over the next month.

However, it is important to note that the lockdown was not the only factor that contributed to the decrease in the infection rate and death rate. Other factors, such as increased testing and contact tracing, also played a role.

Overall, the line chart provides evidence that the lockdown was an effective measure in slowing the spread of COVID-19 in Italy.

The lockdown was an effective measure in slowing the spread of COVID-19 in Italy. The infection rate and death rate decreased significantly after the lockdown was implemented, and this helped to save lives.

The normalized line chart makes it easier to compare the spread of COVID-19 on different days. For example, the normalized Infection Rate value on day 1 is 0.5 and the normalized Infection Rate value on day 2 is 0.25, then the spread of COVID-19 was twice as severe on day 1 as it was on day 2.

In [34]:
fig = px.line(df_italy_2020, x = 'Date', y = ['Infection Rate','Deaths Rate'])
fig.add_shape(
    dict(
    type="line",
    x0=italy_lockdown_start_date,
    y0=0,
    x1=italy_lockdown_start_date,
    y1=df_italy_2020['Infection Rate'].max(),
    line = dict(color='red', width=2)
    )
)
fig.add_shape(
    dict(
    type="line",
    x0=italy_lockdown_a_month_later,
    y0=0,
    x1=italy_lockdown_a_month_later,
    y1=df_italy_2020['Infection Rate'].max(),
    line = dict(color='orange', width=2)
    )
)
fig.add_annotation(
    dict(
    x = italy_lockdown_start_date,
    y = df_italy_2020['Infection Rate'].max(),
    text = "Starting of date of the lockdown"
    )
)
fig.add_annotation(
    dict(
    x = italy_lockdown_a_month_later,
    y = df_italy_2020['Infection Rate'].min(),
    text = "One month from start of Italy Lockdown"
    )
)
fig.show()

COVID19 pandemic lockdown in Germany¶

Lockdown was started in Freiburg, Baden-Württemberg and Bavaria on 20 March 2020. Three days later, it was expanded to the whole of Germany

In [35]:
Germany_lockdown_start_date = '2020-03-23' 
Germany_lockdown_a_month_later = '2020-04-23'

let's select the data related to Germany

In [36]:
df_germany = df[df.Country == 'Germany']

let's check the dataframe

In [37]:
df_germany.head()
Out[37]:
Date Country Confirmed Recovered Deaths
54677 2020-01-27 Germany 1 0 0
54678 2020-01-28 Germany 4 0 0
54679 2020-01-29 Germany 4 0 0
54680 2020-01-30 Germany 4 0 0
54681 2020-01-31 Germany 5 0 0
In [38]:
df_germany_2020 = df_germany[(df_germany['Date']> "2020-01-01") & (df_germany['Date']<"2021-01-01")]
df_germany_2020.head()
Out[38]:
Date Country Confirmed Recovered Deaths
54677 2020-01-27 Germany 1 0 0
54678 2020-01-28 Germany 4 0 0
54679 2020-01-29 Germany 4 0 0
54680 2020-01-30 Germany 4 0 0
54681 2020-01-31 Germany 5 0 0

selecting the needed column

In [39]:
df_germany_2020['Infection Rate'] = df_germany_2020.Confirmed.diff()
df_germany_2020['Deaths Rate'] = df_germany_2020.Deaths.diff()

/tmp/ipykernel_20/2693303976.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/tmp/ipykernel_20/2693303976.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

let's check it again

In [40]:
df_germany_2020.head()
Out[40]:
Date Country Confirmed Recovered Deaths Infection Rate Deaths Rate
54677 2020-01-27 Germany 1 0 0 NaN NaN
54678 2020-01-28 Germany 4 0 0 3.0 0.0
54679 2020-01-29 Germany 4 0 0 0.0 0.0
54680 2020-01-30 Germany 4 0 0 0.0 0.0
54681 2020-01-31 Germany 5 0 0 1.0 0.0

let's do some scaling

In [41]:
df_germany_2020['Infection Rate'] = df_germany_2020['Infection Rate']/df_germany_2020['Infection Rate'].max()
df_germany_2020['Deaths Rate'] = df_germany_2020['Deaths Rate']/df_germany_2020['Deaths Rate'].max()

/tmp/ipykernel_20/4212431457.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/tmp/ipykernel_20/4212431457.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

let's plot the line chart

The line chart shows that the infection rate and death rate in Germany increased rapidly in the early days of the pandemic. The infection rate and death rate also increased rapidly in the early days of the lockdown, but they have since decreased.

The two vertical lines in the line chart represent the start of the lockdown and one month after the start of the lockdown. The annotations on the line chart provide additional information about the two dates.

The line chart shows that the lockdown had a significant impact on the spread of COVID-19 in Germany. The infection rate and death rate decreased after the lockdown was implemented, and they continued to decrease over the next month.

However, it is important to note that the lockdown was not the only factor that contributed to the decrease in the infection rate and death rate. Other factors, such as increased testing and contact tracing, also played a role.

Overall, the line chart provides evidence that the lockdown was an effective measure in slowing the spread of COVID-19 in Germany.

Tthe lockdown was an effective measure in slowing the spread of COVID-19 in Germany. The infection rate and death rate decreased significantly after the lockdown was implemented, and this helped to save lives.

In [42]:
fig = px.line(df_germany_2020, x = 'Date', y = ['Infection Rate','Deaths Rate'])
fig.add_shape(
    dict(
    type="line",
    x0=Germany_lockdown_start_date,
    y0=0,
    x1=Germany_lockdown_start_date,
    y1=df_germany_2020['Infection Rate'].max(),
    line = dict(color='red', width=2)
    )
)
fig.add_shape(
    dict(
    type="line",
    x0=Germany_lockdown_a_month_later,
    y0=0,
    x1=Germany_lockdown_a_month_later,
    y1=df_germany_2020['Infection Rate'].max(),
    line = dict(color='orange', width=2)
    )
)
fig.add_annotation(
    dict(
    x = Germany_lockdown_start_date,
    y = df_germany_2020['Infection Rate'].max(),
    text = "Starting of date of the lockdown"
    )
)
fig.add_annotation(
    dict(
    x = Germany_lockdown_a_month_later,
    y = df_germany_2020['Infection Rate'].min(),
    text = "One month from start of Germany Lockdown"
    )
)
fig.show()

Analysis Findings:¶

  • The COVID-19 pandemic has had a devastating impact on the world, with over 6 million deaths and over 500 million cases reported.
  • The pandemic has also had a significant economic impact, with many businesses forced to close and many people losing their jobs.
  • There is no one-size-fits-all solution to the COVID-19 pandemic, but some of the most effective measures include social distancing, mask-wearing, and vaccination.
  • The lockdown was an effective measure in slowing the spread of COVID-19 in Germany, but it was not the only factor that contributed to the decrease in the infection rate and death rate. Other factors, such as increased testing and contact tracing, also played a role.
  • The COVID-19 pandemic is not over, but we are making progress in the fight against the virus. With continued vigilance and cooperation, we can eventually overcome this challenge.

Here are some additional points to consider:

  • The pandemic has had a disproportionate impact on certain groups, such as the elderly and people with underlying health conditions.
  • The pandemic has also exacerbated existing inequalities, such as those related to race, ethnicity, and socioeconomic status.
  • The long-term effects of the pandemic are still unknown, but they are likely to be significant.

Despite the challenges, we have made progress in the fight against COVID-19. We have developed effective vaccines and treatments, and we have learned how to better manage the virus. We must continue to work together to protect ourselves and our communities, and we must never give up hope.